我在训练一个决策树回归器，但是当我获取特征重要性时，只有数值显示出来。
有谁知道如何获取一个包含变量名称的数据框吗？
以下是代码的主要部分：
num_pipeline = Pipeline([ ('imputer', SimpleImputer(strategy="median")), ('std_scaler', StandardScaler()),])cat_pipeline = Pipeline([ ('imputer', SimpleImputer(strategy="most_frequent")), ('oneHot', OneHotEncoder(handle_unknown='ignore')),])num_attribs = x_train.select_dtypes(include=np.number).columns.tolist()cat_attribs = x_train.select_dtypes(include='object').columns.tolist()full_pipeline = ColumnTransformer([ ("num", num_pipeline, num_attribs), ("cat", cat_pipeline, cat_attribs),])train_prepared = full_pipeline.fit_transform(x_train)param_grid = {'max_leaf_nodes': list(range(2, 100)), 'min_samples_split': [2, 3, 4], 'max_depth': list(range(3, 20))}dtr = DecisionTreeRegressor()grid_search = GridSearchCV(dtr, param_grid, cv=5, scoring='neg_mean_squared_error', verbose=1, return_train_score=True, n_jobs=-1)grid_search = grid_search.fit(train_prepared, y_train)grid_search.best_estimator_.feature_importances_feature_importances_的输出如下：
array([2.59182901e-03, 5.08807106e-04, 1.46808641e-03, 2.20756886e-03, 1.48878361e-01, 5.65411415e-03, 5.16351699e-03, 9.37444882e-03, 0.00000000e+00, 7.19228983e-03, 1.00581364e-03, 1.05073934e-03, 2.63424620e-03, 9.41587243e-03, 7.22742602e-02, 0.00000000e+00, 2.41075666e-03, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.12861715e-02, 3.39987538e-03, 5.27924849e-04, 2.20562317e-03, 4.14808367e-03, 5.82557008e-04, 1.40134963e-03, 0.00000000e+00, 0.00000000e+00, 1.08351677e-03, 0.00000000e+00, 0.00000000e+00, 1.58022433e-03, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.79779634e-02, 5.94436576e-01, 3.72725666e-02, 1.11665462e-03, 2.39049915e-03, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.15314788e-03, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,...])回答：
虽然你不能直接调用模型的方法来获取标签，但它们与x_train的索引方式相同，因此你可以通过以下方式获取名称：
x_train.select_dtypes(include=np.number).columns或者你可以创建一个字典，例如：
feature_importances = {x_train.select_dtypes(include=np.number).columns[x]:grid_search.best_estimator_.feature_importances_[x] for x in range(len(grid_search.best_estimator_.feature_importances_))}